The method of solving this optimization is given in [1]}, which is very clear to follow. However, for professional malicious user detection, there are two limitations for applying metric learning directly: 1) there is no dataset with PMU labels, which means that we cannot build the user subgroup \(S\) . And 2) \(p_j\)  and \(p_k\)  only contain the side information of user \(j,k\) , without considering the interactions (for example, masking strategies of PMUs) in recommender systems. To the best of our knowledge, MMD is the first model that combines HDAN and metric learning for PMU detection with reviews and ratings in recommender systems. Some important notations are shown in Table REF :
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